PROPOSALS

Project 1 Proposal: Integrative Approach to Manhattan Congestion Pricing Data

Presentation for Project 1 Proposal: Presentation Link

Project 1 ICA: In-class Activity Link

End Presentation for Project 1: Presentation Link

Project 2 Proposal <ADD LINK>

Presentation for Project 2 Proposal: Presentation Link

Poster <ADD LINK>

In-class Activity <ADD LINK>

Public Demo <ADD LINK>

ALL OTHER WIKI CONTRIBUTIONS

CONTRIBUTION 1: Added additional genomics datasets to scientific data, Updated links on course year homepage, Reorganized Medical File Types into Scientific Data

CONTRIBUTION 2: Full page detailing pros/cons of various AR development tools

CONTRIBUTION 3: Full page overview of Meta Quest 3

CONTRIBUTION 4: Adding traffic data to scientific data sources

CONTRIBUTION 5: List of Data Visualization Tools in Blender

.....

CONTRIBUTION N [short description] <ADD LINK>

Learning Goals

 before | after
-----------
    3   |  4  | Goal 1: articulate AR/VR visualization software tool goals, requirements, and capabilities
    2   |  4  | Goal 2: construct meaningful evaluation strategies for software libraries, frameworks, and apps
    2   |  5  | Goal 3: execute tool evaluation strategies
    3   |  4  | Goal 4: build visualization software packages
    1   |  4  | Goal 5: comparatively analyze software tools based on evaluation
    3   |  5  | Goal 6: be familiar with a number of AR/VR software tools and hardware
    4   |  5  | Goal 7: think critically about software
    3   |  5  | Goal 8: communicate ideas more clearly
    2   |  4  | Goal 9: establish coherent, understandable process documentation standards
    4   |  5  | Goal 10: have fun :D

Week 1

1/24/2025 - 2 Hours

• Made this journal!

• Got familiarized with Google Sites

• Introduced self on Slack

• Read through some project ideas and the wiki at large

1/25/2025 - 3 Hours

• Read Kenny Gruchalla's bio and brainstormed 3 questions to ask before Tuesday, added to class board doc

• Continued reading through parts of website, identified 3 changes within 3 timescales to make to wiki

• Implemented 3 of the identified changes below

3 Changes, 3 Times

• 10 Minutes:

  • Microsoft open data sets do not link directly to datasets, need updates - Completed

  • Page on medical file types should be reorganized into scientific data for clarity - Completed

  • Several links on Spring 2025 home page go to the wrong place/year, should be corrected - Completed

• 1 hour

  • Social network data should include sources beyond Twitter (Bluesky, Instagram, Facebook, etc)

  • VR evaluation tools should include more methods beyond TLX and SUS

  • Images throughout wiki require alt-text

• 10 hours

  • "What is VR for?" and "Applications of VR" should be consolidated for clarity, large number of pages makes this difficult

  • Dedicated sections for ethics, contemporary issues in VR/AR spaces

  • Prototype some more accessible, sustainable, customizable wiki format beyond Google Sites, such as MediaWiki, Wiki.js, HackMD (full transition likely 10+ hour endeavor) 

Week 2

1/28/2025 - 4 Hours

• Did Quest 3 Setup

• Searched & looked over some past projects

• Potential software to evaluate:

  • VR Chat (multimodal collaborative tool?)

  • 3data

  • WebXR frameworks (focus on cross-device collab?)

    • Babylon.js

    • Three.js

  • Architecture/product rendering?

    • Arkio

    • Twinmotion

  • SentiAR

• Potential project ideas:

  • Collaborative approach to Manhattan Congestion Pricing data (high data dimensionality, available data, could benefit from route/traffic/time overlays, connects with new domains not in wiki)

  • Something to do with the Bloomberg analysis of male YouTubers before the election (high data dim, vr for social sciences, embedding videos could make this dynamic?)

  • Some architectural collaborative ar/vr thing with CAD models. You could have vr for entering the space and ar for having a top-down contextual view. Maybe floorplan overlays too

  • Disease/medical visualization within a body context -- seeing a person on a table and being able to overlay problems and see it in different contexts. External/nervous/circulatory/skeletal etc. Might be good to use published case studies for this?

    • This could also intersect with medical imagry if relevant (x-rays or mris?)

  • Visualizing migration flows + genomic data + location data from human evolution(?) (new domain, data types)

• Added learning goals to journal

Week 3

02/03/2025 - 5.5 Hours

• Completed Bezi Lab (https://bezi.com/play/ffad2484-b393-4af3-ae75-13b70bc262b7)

• Completed Google Earth VR Tutorial (see below)

• Installed DinoVR and Read Associated Paper

• Expanded project ideas:

  • Collaborative approach to Manhattan Congestion Pricing data 

    • list three things you will do during the project:

      1. Investigate and develop methods for collaborative passthrough visualization 

      2. Explore methods for dimensional visualization of time-based data

      3. Create an informational quiz to evaluate AR view against Web formats

    • list one class activity we might do for the project:

      1. In small groups using either the AR or Web software, for three affected routes, determine how much a decrease there was in commute time, if any. Report how you came to this conclusion and how long this took.

    • list potential deliverables:

      1. Expansion and addition to previous work on traffic visualization in AR/VR

      2. Comparison of current visualization software supporting passthrough on Quest 3

      3. Documentation of challenges and methods to approaching data visualization spread over space/time dimensions

  • Something to do with the Bloomberg analysis of male YouTubers before the election

    • list three things you will do during the project:

      1. Connect AR visualization software (or visualization broadly) to embedded media

      2. Evaluating interference between collaboration tools and dynamic media

      3. Leverage network visualization tools to establish and evaluate connection graphs between media figureheads

    • list one class activity we might do for the project:

      1. With a partner, explore an AR space that contains multiple short videos. Then, with a partner, watch multiple short videos as part of a playlist. Discuss your ability to control these videos and whether you felt that you could effectively view them with someone else present.

    • list potential deliverables:

      1. Tutorials on adding embedded media to various visualization software packages

      2. Elaboration on network visualization in VR/AR and comparison in doing so between VR vs. AR

      3. Adding application section for AR in journalism/media. 

  • Disease/medical visualization within a body context -- seeing a person on a table and being able to overlay problems and see it in different contexts. External/nervous/circulatory/skeletal etc. Might be good to use published case studies for this?

    • list three things you will do during the project:

      1. Research diagnosis and visualization efficacy of medical professionals using VR/AR

      2. Establish methods for associating biological visual data with physical parts of the body

      3. Explore collaborative annotation methods 

    • list one class activity we might do for the project:

      1. Examine two visualizations on the body and annotate them. With someone else, review and explain your annotations and make edits. Report on your ability to view the same data, work together, and make edits to someone else's annotation.

    • list potential deliverables:

      1. Create comparison between state of medical visualization industry in VR and AR.

      2. Investigate usage of trackers/markers to stablize virtual objects within a set space

      3. Provide tutorial for using connectivity between HMDs to facilitate collaboration

  • Visualizing migration flows + genomic data + location data from human evolution(?) (new domain, data types)

    • list three things you will do during the project:

      1. Collect novel historical- and demographic-based genomic data

      2. Explore visualization methods and approaches for extremely large datasets (3D dimensionality reduction?)

      3. Connect and adapt specialized software analysis and visualization packages to AR contexts

    • list one class activity we might do for the project:

      1. View a web-based and AR-based view of two different dimensionality reductions, in 2D and 3D respectively. Attempt to establish any groupings or trends you may see in the datasets.

    • list potential deliverables:

      1. Expansion of biological, genomic scientific datasets

      2. Exploration and documentation of common scientific analysis and visualization methods in practice

      3. Provide tutorials on adapting visualization software to leverage passthrough

Google Earth VR:

Google Earth Web:

02/05/2025 - 2 Hours

• Added feedback to peer project ideas

• Asked for assistance on Slack with my own project ideas :)

• Outlined milestones and project outline for potential project:

  • Collaborative approach to Manhattan Congestion Pricing data 

    • list three things you will do during the project:

      • Investigate and develop methods for collaborative passthrough visualization 

      • Explore methods for dimensional visualization of time-based data

      • Create an informational quiz to evaluate AR view against Web formats

    • list one class activity we might do for the project:

      • In small groups using either the AR or Web software, for three affected routes, determine how much a decrease there was in commute time, if any. Report how you came to this conclusion and how long this took.

    • list potential deliverables:

      • Expansion and addition to previous work on traffic visualization in AR/VR

      • Comparison of current visualization software supporting passthrough on Quest 3

      • Documentation of challenges and methods to approaching data visualization spread over space/time dimensions

      • Identify and format dataset to exist in reasonable, consumable format

      • Collect, compare, and modify software packages that may work for complex dataset

      • Produce prototype that facilitates collaborative analysis while displaying data over routes

    • Potential milestones:

      • 2/11: Complied list of visualization software, initial formatting of data

      • 2/13: Comparison list of software after initial investigation, final use of data, tests for collaboration

      • 2/20: Import of data into the software, first test visualization

      • 2/25: Further building of visualization, adding additional models, pilot versions for comparison

      • 2/27: Finalizing visualization, documentation of process, building evaluative tools

      • 3/04: In-class activity! Collect data using evaluative tool, comparing AR and Web visualizations of the dataset.

        • In-class activity: In small groups using either the AR or Web software, for three affected routes, determine how much a decrease there was in commute time, if any. Report how you came to this conclusion and how long this took. Additionally, complete the SUS for both modes. 

        • What evaluative info that activity will collect: Qualitative data about reasoning in AR vs. Web, quantitative data on whether that reasoning is accurate, comparative usability data from SUS metrics 

      • 3/06: Build first-pass analysis of data, documenting methods, overarching conclusions, next steps 

• Completed feedback form 02/04 DinoVR tutorial

Week 4

02/09/2025 - 3 Hours

• Created brief project presentation for class on 2/11

• Uploaded DinoVR photos to class board

• Determined unresolved gaps in wiki for deliverables in project plan

02/10/2025 - 3 Hours

• Created project proposal

• Project self-assessment:

  • Rating scale

    1. strongly disagree

    2. disagree

    3. neither agree nor disagree

    4. agree

    5. strongly agree

  • The proposed project clearly identifies deliverable additions to our VR Software Wiki - 5; multiple possible additions listed

  • Involves passthrough or “augmented” in VR - 4; relies on AR/passthrough for one visualization, but details aren't super finalized

  • The proposed project involves large scientific data visualization along the lines of the "Scientific Data" wiki page and identifies the specific data type and software that it will use - 5; data identified

  • The proposed project has a realistic schedule with explicit and measurable milestones at least each week and mostly every class - 4; some milestones looser than others, typically come with deliverables but some intentional room added

  • The proposed project explicitly evaluates VR software, preferably in comparison to related software - 5; comparison between AR/web, evaluation metrics clear

  • The proposed project includes an in-class activity, which can be formative (early in the project) or evaluative (later in the project) - 4; it exists, needs some fleshing out

  • The proposed project has resources available with sufficient documentation - 4; some software identified but nothing solid quite yet, part of research section of project

• Started list of potential software to drive visualization

02/11/2025 - 3.5 Hours

• Journal Self-Evaluation:

  • Key:

    • 5 == exceeds expectations

    • 4 == meets expectations

    • 3 == mostly solid with some gaps

    • 2 == shows some progress

    • 1 == some attempt

    • 0 == not found

  • Criteria:

  • Journal activities are explicitly and clearly related to course deliverables

    • 4 -- Journal activities reflect deliverables requested in course timeline; project milestones.

  • deliverables are described and attributed in wiki

    • 3 -- more deliverables completed would be nice, but those present are described and linked.

  • report states total amount of time

    • 5 -- there it is at the top!

  • total time is appropriate

    • 4 -- Length of activities align with time taken

• Continued examining potential visualization software w/ initial notes:

  • Two options broadly: Unity or WebXR

    • Also some dark-horse options for development: Vizard, Bezi, Sketchfab, Niantic Studio, Vuforia

      • Most of these are more plug-and-play with restrictions on interactivity

      • Some of these do support collaboration out of the box to varying degrees

      • A few (particularly vizard) actually include documentation referencing passthrough on Quest, which is helpful!

    • In any case, the actual design of the visualization will likely need to be done "by hand" to some degree

      • Likely visualizing the data and porting it into some software to view 

      • Deck.gl is interesting here as a data vis plugin that has a bit of information on how to do webXR

        • Could use this as a driver of data, might minimize the steps involved

      • How to do this might be interesting... Blender with data vis plugins?

        • There are methods to visualize in VR in blender... can you do this in AR?

    • WebXR frameworks seem like the stronger choice to get a prototype out as quickly as possible, even if constrained on interactivity

• Held place for ICA in Course Timeline

02/12/2025 - 2.5 Hours

• Started AR Dev tool comparison wiki page

Week 5

02/18/2025 - 3.5 Hours

• Setup Paraview and Volume Rendering for in-class activity

• Continued working on AR Dev tool comparison wiki page

02/20/2025 - 1 Hour

• APV Unity Lab

Week 6

02/23/2025 - 7.5 Hours

• Finalized visualization programs

• Began loading models, test visualizations

• Experimenting with Blender

• Presentation showing progress on project

02/27/2025 - 4 Hours

• Distilled dataset into useable form

• Finalized Blender workflow

• Began producing models to integrate into final project

Week 7

03/02/2025 - 5.5 Hours

• Finished making models of dataset in Blender

• Ported and placed all models into Bezi

• Finalized, tested, touched-up Bezi models

03/03/2025 - 7 Hours

• Polished Bezi models, attempted to improve performance

• Research for evaluation tools for desktop services and AR tools

• Generated instructions and surveys for ICA

• Research for wiki pages

• Setting up Sidequest, prepping for peers' ICAs

Week 8

03/09/2025 - 5.5 Hours

• Created and published broad overview of AR development software

• Began research for future wiki contributions 

  • Quest 3 AR overview, additions to traffic datasets, overview of evaluation metrics, data visualizations in Blender, modelling with good performance on HMDs, potential miscellaneous edits

03/10/2025 - 5.5 Hours

• Created wiki page providing overview for Meta Quest 3

• Created wiki page providing list of Blender tools for data visualization

• Added traffic data to scientific data list

• Prepped for 3/11/2025 ICA

• Started final project presentation, data analysis

03/11/2025 - 4.5 Hours

• Completed analysis of data from project 1 ICA

• Completed final project 1 presentation, self-evaluation

• Small touch-ups on various wiki contributions

Week 9

03/17/2025 - 3 Hours

• Read Seven Scenarios paper

• Project 2 Ideas

  • Implement AR options in DinoVR

    • Seven scenarios: CDA, CTV, UE

      • DinoVR is notable for being a VR visualization tool that supports interactive collaboration between users. By adding AR support, the ability for people to collaborate (CDA) to communicate information and findings (CTV) could be increased. Additionally, the actual usability of the system may improve (UE), as users may more easily co-locate and contextualize the data in their physical shared environment, rather than in a blank space. 

    • Potential Deliverables:

      • Documentation on accessing cameras, passthrough directly with Quest 3

      • Documentation of networking and share views as implemented in DinoVR

      • Comparison of other collaborative AR tools (Needle) to DinoVR

  • Evaluating text displays in AR environments

    • Seven scenarios: UE, UP

      • This project would examine how text is displayed in AR context in multiple forms, providing guidance on how labels and text might be provided across environments. The result of this project would be a head-to-head assessment of several versions of text display, including novel ones that might take advantage of passthrough technology, relying on controlled experiments with reading speed (UP) along with preferences and percieved comfort in reading (UE). 

    • Potential Deliverables:

      • Documentation of using and modulating passthrough media with the Quest 3

      • Collection of resources on displaying text well in VR and AR; comparison between AR and VR 

      • Tutorial on using Unity for scientific data collection

      • Tutorial on extracting data from Quest 3

  • Disease/medical visualization within a body context -- seeing a person on a table and being able to overlay problems and see it in different context

    • Seven scenarios: CTV, CDA, UE, VDAR

      • Participants would view two different sets of medical data, examining the data as it's placed on a body in AR and viewing similar data in a context-free AR space. Particular attention would be paid to how users understand how the body context might better convey the nature of the data (CTV) and how findings might be generated (VDAR), including in a group (CDA). Participants would also assess the usability of both applications to determine if there is a trade-off between increased context, physical demand, and complexity (UE).

    • Potential Deliverables:

      • Expansion of medical data on scientific data page

      • Create comparison between state of medical visualization industry in VR and AR.

      • Investigate usage of anchors to stabilize virtual objects within a set space, including with direct camera access

      • Provide tutorial for using connectivity between HMDs to facilitate collaboration

  • Evaluating use of embedded media into AR data visualization environments, including occlusion and spatial audio

    • Seven scenarios: UE, CTV

      • Many visualizations have embedded media in some form, including pictures, videos, and audio. This may be because the media itself is the data being visualized (i.e. showing an example video in content analysis) or enhances and explains the visualization in a clearer way (i.e. an audio presentation). How this media is included and what best practices are for doing so are unclear. This project would evaluate best practices for embedding media into visualizations to make them clearest (CTV) and more comfortable and accessible to use (UE).

    • Potential Deliverables:

      • Evaluation  and comparison of media tools in AR and VR

      • Documentation on importing video to AR apps

      • Documentation on using and disabling spatial audio, best practices for doing so

      • Best practices for using video clips in AR (environment dimming, curved screens, etc)

03/18/2025 - 1.5 Hours

• Created project 2 proposal presentation

03/19/2025 - 1.5 Hours

• Project 2 draft proposal:

  • Optimizing Text Displays in AR Environments

    • list three things you will do during the project:

      • Investigate and collect current practices for text display in AR

      • Document processes for using Unity and Quest HMDs as tools for scientific data collection

      • Create a research-quality program to evaluate user performance with text displays

    • list one class activity we might do for the project:

      • Individually, participants will work through the experiment program, reading the set of standardized sentences out loud at a comfortable pace. Participants view several variations on how text is displayed, examining aspects of contrast, color, backgrounds, and outlines in a head-to-head manner. After, participants will complete a survey indicating their text preferences along several factors.

    • list potential deliverables:

      • Documentation of using and modulating passthrough media with the Quest 3

      • Collection of resources on displaying text well in VR and AR; comparison between AR and VR 

      • Tutorial on using Unity for scientific data collection, generating user reports

      • Tutorial on extracting quantitative user data from Quest

      • Best practices on transitioning between AR and VR (environment dimming, removing color) 

    • Potential milestones:

      • 4/01: Collecting best practices in literature, building wiki page synthesizing information and common use

      • 4/03: Demo visualizations for feasibility, further literature review on common practices

      • 4/08: Assessment of potential software for development and deployment, prioritizing interactivity and headset API access for passthrough control

      • 4/10: Setup development software, generate initial text displays in headset

      • 4/15: Further development, all displays and text work and are demoable in a single package

      • 4/17: Interaction added to experiment program, completed minimum viable walkthrough to collect reasonable data

      • 4/22: Process created for extracting quantitative data from the headset, testing and polishing experiment program

      • 4/24: Creating survey evaluations, piloting ICA with individuals to confirm functionality and stability

      • 4/29: ICA! Running experiment in class

        • In-class activity: Individually, walk through the experiment program in your Quest headset. You will see several text displays and will be asked to read the sentences displayed out loud to the best of your ability. After, you will complete 

        • What evaluative info that activity will collect: Quantitative data on reading speed based on how quickly users move between sentences, quantitative and qualitative data on user preferences and associated reasonings 

      • 5/01: Final presentation and data analysis from ICA

• Project 2 proposal self-evaluation:

  • Rating scale

    • 1 -- strongly disagree

    • 2 -- disagree

    • 3 -- neither agree nor disagree

    • 4 -- agree

    • 5 -- strongly agree

  • The proposed project clearly identifies deliverable additions to our VR Software Wiki - 5; multiple possible additions listed, specific pages named

  • Involves passthrough or “augmented” in VR - 5; explicitly evaluates AR

  • The proposed project involves large scientific data visualization along the lines of the "Scientific Data" wiki page and identifies the specific data type and software that it will use - 2; this project largely deals with text, which would be likely be related to labels for data visualization. Adding occlusion as a factor, such as in the DinoVR paper, might help this

  • The proposed project has a realistic schedule with explicit and measurable milestones at least each week and mostly every class - 5; milestones clearly defined, each one appears to have some actionable item attached

  • The proposed project explicitly evaluates VR software, preferably in comparison to related software - 4; evaluates text displays,  which varies significantly between software based on development practice

  • The proposed project includes an in-class activity, which can be formative (early in the project) or evaluative (later in the project) - 5; it exists, evaluative,  project very clearly seems to build to this

  • The proposed project has resources available with sufficient documentation - 4; Unity mentioned as potential development software, some early work identified with literature review, but this may be fleshed out further